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Source code related to the publication "Visualization of loop extrusion by DNA nanoscale tracing in single human cells" by Beckwith, Ødegård-Fougner et.al. DOI: https://doi.org/10.1101/2021.04.12.439407

SVLT = Serving Vision to Living Things. A service for on-demand computer vision, targeted at image feedback in microscopy and other life sciences applications.

A Python client for the high-level DMA API.

GEMsembler tool for assembling and comparing several types of Genome-Scale Metabolic Models (GEMs)

These Python scripts implement binary classification models to i) predict detectability of noncanonical ORF microprotein sequences using a tensorFlow-Keral Classification and ii) predict detectability of HLA predicted peptide binding sequences a Multi-Layer Perceptron (MLP) classifier from scikit-learn. The scripts preprocess the datasets, train an artificial neural network (ANN), evaluate performance using multiple metrics, and extract feature importance based on model weights.

This script implements a machine learning pipeline that integrates the ESM2 transformer-based protein model with a Logistic Regression classifier to predict the detectability of microprotein sequences by Mass Spectrometry. The script preprocesses data, extracts embeddings from sequences, performs feature selection, trains a logistic regression model, and evaluates its performance using multiple metrics.

Development of Automated Protocols on Zeiss microscopes controlled by ZenBlue

Demo of a Poissonian Cellular-Potts model